How to Use the Tally MCP in LlamaIndex

Q: What if I need to check multiple Tally workspaces in LlamaIndex?

You first run listworkspaces and index the resulting list. Then, you can ask a query that references 'all workspaces' or 'the most recent workspace,' making the context immediately available for retrieval.

Q: Can LlamaIndex track changes using Tally submissions?

Yes. You can index the results of getsubmission and deletesubmission. This allows you to build a knowledge base that tracks not just what was submitted, but when it was removed or altered.

Q: Does LlamaIndex support filtering resources when accessing Tally data?

Yes; you can use listforms with a workspace ID filter. When building your RAG application, this ensures that the index only contains data relevant to a specific department or project.

Ground Your Knowledge Base with LlamaIndex: Querying Tally Data Semantically.

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Indexing Tally Workspaces in LlamaIndex

Use `list_workspaces` to get details on all available Tally workspaces, and then index this structural data into your knowledge base. This means you won't just store a list; the system understands what each workspace is for. Later, when querying via LlamaIndex, you can ask natural language questions like 'Which workspace handles HR forms?' and get an answer grounded in the actual API output from `get_workspace`.

Auditing Form Metadata with LlamaIndex

The `list_forms` tool gathers metadata about every Tally form. By indexing this, you can build a RAG application that answers questions like 'What fields does the expense report form have?' without needing to write complex code. This is powerful because if you need to know what data was collected in the past, LlamaIndex combines `get_form` details with historical submission records for a unified answer.

Processing Submissions with LlamaIndex and MCP Server

`list_submissions` retrieves multiple entries, which LlamaIndex then treats as source documents. You can combine this list output with `get_submission` details to create highly detailed knowledge chunks. Instead of just getting a JSON dump, your query engine interprets the data: 'Summarize all submissions related to Project X,' and returns an answer based on the actual API content.

Setup guide

Set up Tally MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Tally MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Tally tools.",
)
response = await agent.run("List recent Tally data")

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Common questions about Tally MCP in LlamaIndex

LlamaIndex indexes your Tally data, turning structured metadata into searchable knowledge. You can query past forms or submissions using natural language to understand their context without running manual scripts.

You first run `list_workspaces` and index the resulting list. Then, you can ask a query that references 'all workspaces' or 'the most recent workspace,' making the context immediately available for retrieval.

Yes. You can index the results of `get_submission` and `delete_submission`. This allows you to build a knowledge base that tracks not just what was submitted, but when it was removed or altered.

It primarily indexes form metadata and submission records. The exact data types include fields from Tally forms and the structured content of submitted records, making them available for semantic search.

Yes; you can use `list_forms` with a workspace ID filter. When building your RAG application, this ensures that the index only contains data relevant to a specific department or project.

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